Overview

Dataset statistics

Number of variables11
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory91.6 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical2

Alerts

storm has constant value ""Constant
400kmDensity is highly overall correlated with SYM/H_INDEX_nT and 4 other fieldsHigh correlation
SYM/H_INDEX_nT is highly overall correlated with 400kmDensityHigh correlation
1-M_AE_nT is highly overall correlated with SYM/H_INDEX_nTHigh correlation
DAILY_SUNSPOT_NO_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
DAILY_F10.7_ is highly overall correlated with DAILY_SUNSPOT_NO_ and 3 other fieldsHigh correlation
SOLAR_LYMAN-ALPHA_W/m^2 is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
mg_index (core to wing ratio (unitless)) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
irradiance (W/m^2/nm) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
SYM/H_INDEX_nT has 16686 (1.7%) zerosZeros
DAILY_SUNSPOT_NO_ has 180460 (18.0%) zerosZeros
d_diff has 16724 (1.7%) zerosZeros

Reproduction

Analysis started2023-02-24 21:38:44.149519
Analysis finished2023-02-24 21:39:36.886729
Duration52.74 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

400kmDensity
Real number (ℝ)

Distinct933933
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7484057 × 10-12
Minimum5.77655 × 10-16
Maximum2.503598 × 10-11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:39:36.963497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5.77655 × 10-16
5-th percentile2.6951312 × 10-13
Q16.5198245 × 10-13
median1.2339175 × 10-12
Q32.3486263 × 10-12
95-th percentile4.985103 × 10-12
Maximum2.503598 × 10-11
Range2.5035402 × 10-11
Interquartile range (IQR)1.6966438 × 10-12

Descriptive statistics

Standard deviation1.556046 × 10-12
Coefficient of variation (CV)0.88997993
Kurtosis0
Mean1.7484057 × 10-12
Median Absolute Deviation (MAD)7.090118 × 10-13
Skewness0
Sum1.7484057 × 10-6
Variance2.4212791 × 10-24
MonotonicityNot monotonic
2023-02-24T16:39:37.101130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.310671 × 10-126
 
< 0.1%
1.352712 × 10-126
 
< 0.1%
1.365907 × 10-125
 
< 0.1%
1.26003 × 10-125
 
< 0.1%
1.00551 × 10-125
 
< 0.1%
1.6048 × 10-125
 
< 0.1%
1.245372 × 10-125
 
< 0.1%
1.097129 × 10-125
 
< 0.1%
1.152148 × 10-125
 
< 0.1%
1.212032 × 10-125
 
< 0.1%
Other values (933923) 999948
> 99.9%
ValueCountFrequency (%)
5.77655 × 10-161
< 0.1%
1.105662 × 10-151
< 0.1%
1.174258 × 10-151
< 0.1%
1.211596 × 10-151
< 0.1%
1.548175 × 10-151
< 0.1%
1.576348 × 10-151
< 0.1%
1.659292 × 10-151
< 0.1%
1.950617 × 10-151
< 0.1%
2.274078 × 10-151
< 0.1%
3.031177 × 10-151
< 0.1%
ValueCountFrequency (%)
2.503598 × 10-111
< 0.1%
2.499609 × 10-111
< 0.1%
2.254039 × 10-111
< 0.1%
2.24411 × 10-111
< 0.1%
2.226916 × 10-111
< 0.1%
2.217619 × 10-111
< 0.1%
2.217417 × 10-111
< 0.1%
2.201916 × 10-111
< 0.1%
2.163607 × 10-111
< 0.1%
2.161917 × 10-111
< 0.1%

SYM/H_INDEX_nT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct570
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-16.16863
Minimum-490
Maximum146
Zeros16686
Zeros (%)1.7%
Negative819194
Negative (%)81.9%
Memory size15.3 MiB
2023-02-24T16:39:37.226817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-490
5-th percentile-51
Q1-24
median-13
Q3-4
95-th percentile10
Maximum146
Range636
Interquartile range (IQR)20

Descriptive statistics

Standard deviation22.810829
Coefficient of variation (CV)-1.4108078
Kurtosis44.245328
Mean-16.16863
Median Absolute Deviation (MAD)10
Skewness-4.0341271
Sum-16168630
Variance520.33394
MonotonicityNot monotonic
2023-02-24T16:39:37.349494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 27703
 
2.8%
-9 27643
 
2.8%
-13 27561
 
2.8%
-8 27230
 
2.7%
-14 27227
 
2.7%
-15 27016
 
2.7%
-12 27009
 
2.7%
-11 26944
 
2.7%
-7 26449
 
2.6%
-16 25545
 
2.6%
Other values (560) 729673
73.0%
ValueCountFrequency (%)
-490 1
< 0.1%
-487 1
< 0.1%
-486 1
< 0.1%
-485 1
< 0.1%
-483 1
< 0.1%
-479 1
< 0.1%
-477 1
< 0.1%
-475 1
< 0.1%
-470 2
< 0.1%
-469 1
< 0.1%
ValueCountFrequency (%)
146 1
< 0.1%
136 1
< 0.1%
134 1
< 0.1%
132 1
< 0.1%
130 1
< 0.1%
129 1
< 0.1%
128 1
< 0.1%
127 1
< 0.1%
124 1
< 0.1%
123 1
< 0.1%

1-M_AE_nT
Real number (ℝ)

Distinct2268
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.45332
Minimum1
Maximum4174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:39:37.484128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q153
median138
Q3327
95-th percentile727
Maximum4174
Range4173
Interquartile range (IQR)274

Descriptive statistics

Standard deviation245.9207
Coefficient of variation (CV)1.0717679
Kurtosis7.0681008
Mean229.45332
Median Absolute Deviation (MAD)101
Skewness2.0864789
Sum2.2945332 × 108
Variance60476.992
MonotonicityNot monotonic
2023-02-24T16:39:37.604783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 6945
 
0.7%
35 6795
 
0.7%
32 6789
 
0.7%
31 6774
 
0.7%
37 6764
 
0.7%
33 6739
 
0.7%
39 6736
 
0.7%
36 6701
 
0.7%
30 6614
 
0.7%
41 6566
 
0.7%
Other values (2258) 932577
93.3%
ValueCountFrequency (%)
1 18
 
< 0.1%
2 76
 
< 0.1%
3 211
 
< 0.1%
4 442
 
< 0.1%
5 749
 
0.1%
6 1055
0.1%
7 1244
0.1%
8 1506
0.2%
9 1889
0.2%
10 2193
0.2%
ValueCountFrequency (%)
4174 1
< 0.1%
4056 1
< 0.1%
3708 1
< 0.1%
3698 1
< 0.1%
3680 1
< 0.1%
3570 1
< 0.1%
3568 1
< 0.1%
3481 1
< 0.1%
3454 1
< 0.1%
3452 1
< 0.1%

DAILY_SUNSPOT_NO_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct205
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.909605
Minimum0
Maximum281
Zeros180460
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:39:37.725488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median45
Q386
95-th percentile153
Maximum281
Range281
Interquartile range (IQR)73

Descriptive statistics

Standard deviation52.400122
Coefficient of variation (CV)0.93722935
Kurtosis1.1762638
Mean55.909605
Median Absolute Deviation (MAD)33
Skewness1.1388535
Sum55909605
Variance2745.7728
MonotonicityNot monotonic
2023-02-24T16:39:37.858106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 180460
 
18.0%
13 29017
 
2.9%
15 18726
 
1.9%
12 18270
 
1.8%
18 14296
 
1.4%
16 13576
 
1.4%
14 12448
 
1.2%
26 12285
 
1.2%
11 12066
 
1.2%
58 11379
 
1.1%
Other values (195) 677477
67.7%
ValueCountFrequency (%)
0 180460
18.0%
6 1285
 
0.1%
7 594
 
0.1%
8 575
 
0.1%
9 4279
 
0.4%
10 6403
 
0.6%
11 12066
 
1.2%
12 18270
 
1.8%
13 29017
 
2.9%
14 12448
 
1.2%
ValueCountFrequency (%)
281 455
< 0.1%
279 539
0.1%
270 458
< 0.1%
267 551
0.1%
263 503
0.1%
252 534
0.1%
250 1124
0.1%
248 1089
0.1%
247 1082
0.1%
239 546
0.1%

DAILY_F10.7_
Real number (ℝ)

Distinct821
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.13738
Minimum65.1
Maximum999.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:39:37.984794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum65.1
5-th percentile67.9
Q174.5
median91.8
Q3118.7
95-th percentile166.4
Maximum999.9
Range934.8
Interquartile range (IQR)44.2

Descriptive statistics

Standard deviation64.458589
Coefficient of variation (CV)0.61897647
Kurtosis142.26382
Mean104.13738
Median Absolute Deviation (MAD)19.9
Skewness10.560106
Sum1.0413738 × 108
Variance4154.9097
MonotonicityNot monotonic
2023-02-24T16:39:38.108469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.3 8141
 
0.8%
68 6135
 
0.6%
67.4 5980
 
0.6%
68.8 5936
 
0.6%
70.8 5875
 
0.6%
70 5671
 
0.6%
70.5 5362
 
0.5%
69.5 4990
 
0.5%
69.8 4915
 
0.5%
68.5 4829
 
0.5%
Other values (811) 942166
94.2%
ValueCountFrequency (%)
65.1 539
 
0.1%
65.9 576
 
0.1%
66.1 170
 
< 0.1%
66.2 2196
0.2%
66.3 1601
0.2%
66.4 2109
0.2%
66.5 1053
0.1%
66.6 1383
0.1%
66.7 2163
0.2%
66.8 2479
0.2%
ValueCountFrequency (%)
999.9 3887
0.4%
275.4 587
 
0.1%
270.9 537
 
0.1%
267.6 537
 
0.1%
246.9 458
 
< 0.1%
245.2 546
 
0.1%
242.6 516
 
0.1%
232.8 561
 
0.1%
232.3 455
 
< 0.1%
229.5 529
 
0.1%

SOLAR_LYMAN-ALPHA_W/m^2
Real number (ℝ)

Distinct1333
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0069723484
Minimum0.005898
Maximum0.009751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:39:38.236094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.005898
5-th percentile0.006003
Q10.0063
median0.006848
Q30.007503
95-th percentile0.008478
Maximum0.009751
Range0.003853
Interquartile range (IQR)0.001203

Descriptive statistics

Standard deviation0.00079261697
Coefficient of variation (CV)0.11368006
Kurtosis-0.066324399
Mean0.0069723484
Median Absolute Deviation (MAD)0.000579
Skewness0.75099245
Sum6972.3484
Variance6.2824166 × 10-7
MonotonicityNot monotonic
2023-02-24T16:39:38.364751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.005961 2882
 
0.3%
0.006659 2764
 
0.3%
0.006479 2757
 
0.3%
0.006008 2737
 
0.3%
0.00636 2669
 
0.3%
0.006031 2544
 
0.3%
0.006016 2467
 
0.2%
0.006019 2334
 
0.2%
0.005991 2255
 
0.2%
0.005953 2243
 
0.2%
Other values (1323) 974348
97.4%
ValueCountFrequency (%)
0.005898 553
0.1%
0.005908 550
0.1%
0.005912 520
0.1%
0.005918 560
0.1%
0.005926 545
0.1%
0.00594 564
0.1%
0.005941 572
0.1%
0.005942 567
0.1%
0.005943 550
0.1%
0.005946 1205
0.1%
ValueCountFrequency (%)
0.009751 561
0.1%
0.00974 516
0.1%
0.00972 522
0.1%
0.009662 458
< 0.1%
0.009581 557
0.1%
0.009577 455
< 0.1%
0.009483 503
0.1%
0.009459 539
0.1%
0.009429 245
< 0.1%
0.009374 551
0.1%
Distinct1440
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26919881
Minimum0.26299
Maximum0.28494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:39:38.505402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.26299
5-th percentile0.26383742
Q10.26510999
median0.26833994
Q30.27250001
95-th percentile0.27787
Maximum0.28494
Range0.02195
Interquartile range (IQR)0.00739002

Descriptive statistics

Standard deviation0.0045775434
Coefficient of variation (CV)0.017004323
Kurtosis-0.26029184
Mean0.26919881
Median Absolute Deviation (MAD)0.00352006
Skewness0.72967207
Sum269198.81
Variance2.0953904 × 10-5
MonotonicityNot monotonic
2023-02-24T16:39:38.624057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26708001 4011
 
0.4%
0.26418999 3344
 
0.3%
0.26396 3244
 
0.3%
0.26434001 2783
 
0.3%
0.26594999 2778
 
0.3%
0.26391 2774
 
0.3%
0.26576 2727
 
0.3%
0.26712999 2712
 
0.3%
0.26567999 2695
 
0.3%
0.26565999 2694
 
0.3%
Other values (1430) 970238
97.0%
ValueCountFrequency (%)
0.26299 170
 
< 0.1%
0.26312 528
0.1%
0.26313001 520
0.1%
0.26315001 1110
0.1%
0.26317999 321
 
< 0.1%
0.26319 22
 
< 0.1%
0.26320001 1057
0.1%
0.26321 628
0.1%
0.26322001 557
0.1%
0.26323 1101
0.1%
ValueCountFrequency (%)
0.28494 516
0.1%
0.28485999 561
0.1%
0.28428999 458
< 0.1%
0.28426999 522
0.1%
0.2841 455
< 0.1%
0.28373 245
< 0.1%
0.28360999 552
0.1%
0.28347999 503
0.1%
0.2834 539
0.1%
0.28323999 557
0.1%

irradiance (W/m^2/nm)
Real number (ℝ)

Distinct1998
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0056221117
Minimum0.0048813247
Maximum0.0073493496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:39:38.751739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0048813247
5-th percentile0.0049377489
Q10.0051511205
median0.0055406117
Q30.0059995502
95-th percentile0.0067077135
Maximum0.0073493496
Range0.0024680248
Interquartile range (IQR)0.00084842974

Descriptive statistics

Standard deviation0.00055822392
Coefficient of variation (CV)0.099290791
Kurtosis-0.37944516
Mean0.0056221117
Median Absolute Deviation (MAD)0.00041343644
Skewness0.67582942
Sum5622.1117
Variance3.1161395 × 10-7
MonotonicityNot monotonic
2023-02-24T16:39:38.879404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004975144286 1110
 
0.1%
0.005085965153 1109
 
0.1%
0.00498051988 1092
 
0.1%
0.004922427703 1091
 
0.1%
0.005260170903 1087
 
0.1%
0.004940496758 1077
 
0.1%
0.005658049602 1077
 
0.1%
0.004946734291 1052
 
0.1%
0.004955403507 1032
 
0.1%
0.004939651117 872
 
0.1%
Other values (1988) 989401
98.9%
ValueCountFrequency (%)
0.004881324712 254
< 0.1%
0.00488556223 520
0.1%
0.004886395764 517
0.1%
0.004887722898 579
0.1%
0.004894145299 574
0.1%
0.00489506498 528
0.1%
0.004896449856 571
0.1%
0.004898893181 568
0.1%
0.004898921587 411
< 0.1%
0.004900876433 273
< 0.1%
ValueCountFrequency (%)
0.007349349558 546
0.1%
0.00734248152 545
0.1%
0.007334709167 537
0.1%
0.007301890757 519
0.1%
0.007266042288 562
0.1%
0.007257604506 391
< 0.1%
0.007208690513 477
< 0.1%
0.007195423823 549
0.1%
0.007178029511 242
< 0.1%
0.007172006648 62
 
< 0.1%

storm
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
1
1000000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1000000
100.0%

Length

2023-02-24T16:39:38.986117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T16:39:39.081857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1000000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1000000
100.0%

storm phase
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
2
604726 
1
395274 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 604726
60.5%
1 395274
39.5%

Length

2023-02-24T16:39:39.154667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T16:39:39.250410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 604726
60.5%
1 395274
39.5%

Most occurring characters

ValueCountFrequency (%)
2 604726
60.5%
1 395274
39.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 604726
60.5%
1 395274
39.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 604726
60.5%
1 395274
39.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 604726
60.5%
1 395274
39.5%

d_diff
Real number (ℝ)

Distinct816208
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1204068 × 10-17
Minimum-1.1317823 × 10-11
Maximum1.0747433 × 10-11
Zeros16724
Zeros (%)1.7%
Negative479935
Negative (%)48.0%
Memory size15.3 MiB
2023-02-24T16:39:39.361116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1317823 × 10-11
5-th percentile-2.0239725 × 10-13
Q1-4.298 × 10-14
median4.589 × 10-16
Q34.5119 × 10-14
95-th percentile1.9625305 × 10-13
Maximum1.0747433 × 10-11
Range2.2065256 × 10-11
Interquartile range (IQR)8.8099 × 10-14

Descriptive statistics

Standard deviation2.0815659 × 10-13
Coefficient of variation (CV)9816.8237
Kurtosis0
Mean2.1204068 × 10-17
Median Absolute Deviation (MAD)4.404935 × 10-14
Skewness0
Sum2.1204068 × 10-11
Variance4.3329167 × 10-26
MonotonicityNot monotonic
2023-02-24T16:39:39.483785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16724
 
1.7%
8.692 × 10-1511
 
< 0.1%
3.4701 × 10-1411
 
< 0.1%
-7.702 × 10-1510
 
< 0.1%
-1.123 × 10-1510
 
< 0.1%
-2.847 × 10-159
 
< 0.1%
9.978 × 10-159
 
< 0.1%
-5.547 × 10-159
 
< 0.1%
2.4583 × 10-149
 
< 0.1%
9.25 × 10-169
 
< 0.1%
Other values (816198) 983189
98.3%
ValueCountFrequency (%)
-1.1317823 × 10-111
< 0.1%
-9.4411153 × 10-121
< 0.1%
-9.03666 × 10-121
< 0.1%
-7.847266 × 10-121
< 0.1%
-7.8236402 × 10-121
< 0.1%
-7.8011442 × 10-121
< 0.1%
-7.406176 × 10-121
< 0.1%
-7.316718 × 10-121
< 0.1%
-7.241425 × 10-121
< 0.1%
-7.215517 × 10-121
< 0.1%
ValueCountFrequency (%)
1.0747433 × 10-111
< 0.1%
9.7292573 × 10-121
< 0.1%
8.884224 × 10-121
< 0.1%
8.742143 × 10-121
< 0.1%
8.235121 × 10-121
< 0.1%
7.728122 × 10-121
< 0.1%
7.3289078 × 10-121
< 0.1%
7.12723 × 10-121
< 0.1%
7.04828 × 10-121
< 0.1%
6.9862178 × 10-121
< 0.1%

Interactions

2023-02-24T16:39:32.469491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:17.559682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:19.565320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:21.405401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-24T16:39:28.785341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-24T16:39:32.878398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:18.056354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:19.965252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:21.808327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:23.642420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:25.479508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:27.307621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:29.205218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:31.058266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:33.069886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:18.271780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:20.169707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:22.005798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:23.835906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:25.673989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:27.511078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:29.406682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:31.258730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:33.268358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:18.498173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:20.376155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:22.205263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:24.036369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:25.868469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:27.721517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:29.613130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:31.464178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:33.459843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:18.715592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:20.574622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:22.398745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:24.238826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:26.060954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:27.929960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:29.812596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:31.660654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:33.673273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:18.953955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:20.791043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:22.610180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:24.459237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:26.273389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:28.154358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:30.028019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:31.876078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:33.867752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:19.162400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:20.996494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:22.813637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:24.665684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:26.472852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-24T16:39:32.076542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:34.060239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:19.364856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:21.201945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:23.026071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:24.869140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:26.675311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:28.576230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:30.436926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:39:32.277006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-24T16:39:39.590491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)d_diffstorm phase
400kmDensity1.000-0.2890.3490.7590.8080.8390.8190.8500.0510.017
SYM/H_INDEX_nT-0.2891.000-0.535-0.156-0.158-0.165-0.155-0.162-0.0060.135
1-M_AE_nT0.349-0.5351.0000.2520.2640.2720.2410.2800.0070.040
DAILY_SUNSPOT_NO_0.759-0.1560.2521.0000.9350.9030.9040.8880.0090.076
DAILY_F10.7_0.808-0.1580.2640.9351.0000.9460.9380.9380.0090.059
SOLAR_LYMAN-ALPHA_W/m^20.839-0.1650.2720.9030.9461.0000.9640.9900.0090.067
mg_index (core to wing ratio (unitless))0.819-0.1550.2410.9040.9380.9641.0000.9520.0080.058
irradiance (W/m^2/nm)0.850-0.1620.2800.8880.9380.9900.9521.0000.0090.086
d_diff0.051-0.0060.0070.0090.0090.0090.0080.0091.0000.005
storm phase0.0170.1350.0400.0760.0590.0670.0580.0860.0051.000
2023-02-24T16:39:39.763040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.3460.2750.7030.3640.7880.7670.801NaN0.0160.067
SYM/H_INDEX_nT-0.3461.000-0.516-0.163-0.080-0.168-0.159-0.161NaN-0.267-0.004
1-M_AE_nT0.275-0.5161.0000.1910.0870.2040.1800.211NaN0.0220.003
DAILY_SUNSPOT_NO_0.703-0.1630.1911.0000.4370.8940.8950.870NaN-0.017-0.000
DAILY_F10.7_0.364-0.0800.0870.4371.0000.4560.4510.446NaN0.0180.000
SOLAR_LYMAN-ALPHA_W/m^20.788-0.1680.2040.8940.4561.0000.9660.984NaN-0.0070.000
mg_index (core to wing ratio (unitless))0.767-0.1590.1800.8950.4510.9661.0000.948NaN-0.012-0.000
irradiance (W/m^2/nm)0.801-0.1610.2110.8700.4460.9840.9481.000NaN-0.0180.000
stormNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
storm phase0.016-0.2670.022-0.0170.018-0.007-0.012-0.018NaN1.000-0.001
d_diff0.067-0.0040.003-0.0000.0000.000-0.0000.000NaN-0.0011.000
2023-02-24T16:39:39.948545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.2890.3490.7590.8080.8390.8190.850NaN0.0270.051
SYM/H_INDEX_nT-0.2891.000-0.535-0.156-0.158-0.165-0.155-0.162NaN-0.385-0.006
1-M_AE_nT0.349-0.5351.0000.2520.2640.2720.2410.280NaN0.0860.007
DAILY_SUNSPOT_NO_0.759-0.1560.2521.0000.9350.9030.9040.888NaN-0.0280.009
DAILY_F10.7_0.808-0.1580.2640.9351.0000.9460.9380.938NaN-0.0320.009
SOLAR_LYMAN-ALPHA_W/m^20.839-0.1650.2720.9030.9461.0000.9640.990NaN-0.0170.009
mg_index (core to wing ratio (unitless))0.819-0.1550.2410.9040.9380.9641.0000.952NaN-0.0250.008
irradiance (W/m^2/nm)0.850-0.1620.2800.8880.9380.9900.9521.000NaN-0.0230.009
stormNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
storm phase0.027-0.3850.086-0.028-0.032-0.017-0.025-0.023NaN1.000-0.001
d_diff0.051-0.0060.0070.0090.0090.0090.0080.009NaN-0.0011.000
2023-02-24T16:39:40.138009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.1980.2370.5610.6050.6380.6120.651NaN0.0220.036
SYM/H_INDEX_nT-0.1981.000-0.377-0.107-0.107-0.111-0.104-0.109NaN-0.317-0.004
1-M_AE_nT0.237-0.3771.0000.1720.1770.1820.1610.187NaN0.0700.005
DAILY_SUNSPOT_NO_0.561-0.1070.1721.0000.7890.7330.7300.712NaN-0.0230.006
DAILY_F10.7_0.605-0.1070.1770.7891.0000.7980.7810.784NaN-0.0260.006
SOLAR_LYMAN-ALPHA_W/m^20.638-0.1110.1820.7330.7981.0000.8320.919NaN-0.0140.006
mg_index (core to wing ratio (unitless))0.612-0.1040.1610.7300.7810.8321.0000.802NaN-0.0210.005
irradiance (W/m^2/nm)0.651-0.1090.1870.7120.7840.9190.8021.000NaN-0.0190.006
stormNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaN
storm phase0.022-0.3170.070-0.023-0.026-0.014-0.021-0.019NaN1.000-0.001
d_diff0.036-0.0040.0050.0060.0060.0060.0050.006NaN-0.0011.000
2023-02-24T16:39:40.322548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)storm phased_diff
400kmDensity1.0000.6000.2530.6090.4910.6750.6600.6820.0220.242
SYM/H_INDEX_nT0.6001.0000.4370.3020.3180.2090.2190.2090.1760.166
1-M_AE_nT0.2530.4371.0000.2140.1470.2070.2010.2000.0520.157
DAILY_SUNSPOT_NO_0.6090.3020.2141.0000.7110.8900.8770.8580.0990.168
DAILY_F10.7_0.4910.3180.1470.7111.0000.6680.6350.6010.0890.098
SOLAR_LYMAN-ALPHA_W/m^20.6750.2090.2070.8900.6681.0000.9640.9700.0870.187
mg_index (core to wing ratio (unitless))0.6600.2190.2010.8770.6350.9641.0000.9370.0760.178
irradiance (W/m^2/nm)0.6820.2090.2000.8580.6010.9700.9371.0000.1120.189
storm phase0.0220.1760.0520.0990.0890.0870.0760.1121.0000.006
d_diff0.2420.1660.1570.1680.0980.1870.1780.1890.0061.000

Missing values

2023-02-24T16:39:34.206846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-24T16:39:34.706512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
37412403.289343e-12-41.0317.059.098.40.0073620.2703700.005854124.167400e-14
20393781.775266e-12-18.0521.088.0109.40.0071710.2734470.00579512-9.917000e-15
21956941.073186e-1216.036.0100.0126.50.0072040.2708600.005737111.543100e-14
35099991.072525e-12-9.030.00.069.60.0062170.2645480.00508312-2.815200e-14
43469752.856909e-122.072.0110.0118.60.0078120.2735300.00606311-2.299000e-15
33612162.690566e-12-88.0848.0130.0154.10.0076480.2735800.005993123.181650e-13
36857423.013006e-12-48.0429.0146.0173.50.0082750.2760400.00661012-7.830000e-16
24981611.296189e-131.040.00.069.20.0060380.2646100.00496612-3.605200e-15
44691273.748893e-12-18.060.0123.0119.40.0072490.2738110.00576912-1.071570e-13
11340744.407488e-12-7.055.087.0113.10.0075630.2761960.005931119.007300e-14
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
20311176.592713e-12-87.0128.0110.0143.50.0073710.2744220.005913124.166000e-15
11124151.865472e-12-10.026.061.0106.10.0069910.2724060.005613124.885000e-15
44976971.032639e-12-12.0128.010.066.60.0059530.2638570.00490812-1.140600e-14
21062037.141121e-122.043.0134.0153.90.0079930.2788040.006337112.237000e-15
3540561.016659e-12-1.0206.0101.0117.90.0070580.2691300.005703111.479811e-13
10041021.157464e-12-20.01023.048.085.60.0069720.2685800.00555211-4.974600e-14
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